Cellular Neural Networks or Cellular Nonlinear Networks (CNN) have been applied to many different fields and problems including, but limited to, image processing since 1988. However, most of the prior art CNN approaches are either based on software solutions (e.g., Convolutional Neural Networks, Recurrent Neural Networks, etc.) or based on hardware that are designed for other purposes (e.g., graphic processing, general computation, etc.). As a result, CNN prior approaches are too slow in term of computational speed and/or too expensive thereby impractical for processing large amount of imagery data. The imagery data can be from any two-dimensional data (e.g., still photo, picture, a frame of a video stream, converted form of voice data, etc.). One of the solutions is to perform convolutional operations in a hardware, for example, Application Specific Integrated Circuit (ASIC).
Practical usages of image processing in a deep learning include, but are not limited to, object detection, object recognition, etc. Prior art solutions have been software based solutions, which contain problem with slowness and require large computation resources. Sometimes, the solution requires many remote computers connected with a either wired or wireless network.
Therefore, it would be desirable to have an improved deep learning object detection and recognition system that overcomes the aforementioned shortcomings, setbacks and/or problems.